270 citations · 591 across the 48 of their papers we have counts for
9 papers · 1 filter
Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization
Zhiyuan Ma, Jiaming Li, Lingzhen Li +7
Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. However, reward-maximizing RL…
Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving
Xin Zhou, Zongchuang Zhao, Zhibo Yang +13
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-langu…
TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM
Hengyi Xie, Chenfei Yao, Xianjin Wu +4
Vision-language-action (VLA) models commonly adopt an LLM-centric pathway, processing visual observations and language instructions through a large language model b…
Wat3R: Underwater 3D Geometry Learning without Annotations
Jiangwei Ren, Xingyu Jiang, Zijie Song +4
Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pion…
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation
Xin Zhou, Dingkang Liang, Xiwu Chen +4
Driving world models serve as a pivotal technology for autonomous driving by simulating environmental dynamics. However, existing approaches predominantly focus on future scene gen…
PointTPA: Dynamic Network Parameter Adaptation for 3D Scene Understanding
Siyuan Liu, Chaoqun Zheng, Xin Zhou +3
Scene-level point cloud understanding remains challenging due to diverse geometries, imbalanced category distributions, and highly varied spatial layouts. Existing methods improve…
ThinkOmni: Lifting Textual Reasoning to Omni-modal Scenarios via Guidance Decoding
Yiran Guan, Sifan Tu, Dingkang Liang +6
Omni-modal reasoning is essential for intelligent systems to understand and draw inferences from diverse data sources. While existing omni-modal large language models (OLLM) excel…
Towards Generalizable Robotic Manipulation in Dynamic Environments
Heng Fang, Shangru Li, Shuhan Wang +3
Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of d…
Video Streaming Thinking: VideoLLMs Can Watch and Think Simultaneously
Yiran Guan, Liang Yin, Dingkang Liang +5
Online Video Large Language Models (VideoLLMs) play a critical role in supporting responsive, real-time interaction. Existing methods focus on streaming perception, lacking a synch…